Summary of Target Detection Algorithms

Li, Wei; Feng, Xiang; Zha, Kaiwen; Li, Shuning; Zhu, Hua

doi:10.1088/1742-6596/1757/1/012003

Cited by 28 publications

(18 citation statements)

References 6 publications

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“…The YOLOv4 network has better detection accuracy and detection speed ( Li et al., 2021 ). Although the YOLOv4 backbone network CSPDarknet53 can effectively extract depth feature information, the limitation of the number of parameters and computational resources leads to difficulties in applying it in practical agricultural production.…”

Section: Methodsmentioning

confidence: 99%

Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model

et al. 2022

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Accurate and rapid identification of the effective number of panicles per unit area is crucial for the assessment of rice yield. As part of agricultural development, manual observation of effective panicles in the paddy field is being replaced by unmanned aerial vehicle (UAV) imaging combined with target detection modeling. However, UAV images of panicles of curved hybrid Indica rice in complex field environments are characterized by overlapping, blocking, and dense distribution, imposing challenges on rice panicle detection models. This paper proposes a universal curved panicle detection method by combining UAV images of different types of hybrid Indica rice panicles (leaf-above-spike, spike-above-leaf, and middle type) from four ecological sites using an improved You Only Look Once version 4 (YOLOv4) model. MobileNetv2 is used as the backbone feature extraction network based on a lightweight model in addition to a focal loss and convolutional block attention module for improved detection of curved rice panicles of different varieties. Moreover, soft non-maximum suppression is used to address rice panicle occlusion in the dataset. This model yields a single image detection rate of 44.46 FPS, and mean average precision, recall, and F1 values of 90.32%, 82.36%, and 0.89%, respectively. This represents an increase of 6.2%, 0.12%, and 16.24% from those of the original YOLOv4 model, respectively. The model exhibits superior performance in identifying different strain types in mixed and independent datasets, indicating its feasibility as a general model for detection of different types of rice panicles in the heading stage.

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Section: Methodsmentioning

confidence: 99%

Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model

et al. 2022

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“…Finally, the distance is measured through the detection information and the depth information provided by the camera to determine the human position. [11][12]. The deep learning detection algorithm realizes object detection through the selflearning characteristics of multilayer convolutional neural network.…”

Section: Human Position Detection Methods Based Onmentioning

confidence: 99%

Human Position Detection Based on Depth Camera Image Information in Mechanical Safety

Zhou

Ren

Zhang

et al. 2022

Advances in Mathematical Physics

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The devices used for human position detection in mechanical safety mainly include safety light curtain, safety laser scanner, safety pad, and vision system. However, these devices may be bypassed when used, and human or equipment cannot be distinguished. To solve this problem, a depth camera is proposed as a human position detection device in mechanical safety. The process of human position detection based on depth camera image information is given; it mainly includes image information acquisition, human presence detection, and distance measurement. Meanwhile, a human position detection method based on Intel RealSense depth camera and MobileNet-SSD algorithm is proposed and applied to robot safety protection. The result shows that the image information collected by the depth camera can detect the human position in real time, which can replace the existing mechanical safety human position detection device. At the same time, the depth camera can detect only human but not mobile devices and realize the separation and early warning of people and mobile devices.

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“…The single-stage algorithms, such as SSD and YOLO series algorithms, are faster but less accurate due to the end-to-end training method. Currently, YOLOv5 is increasingly used to study specific topics, and the improved algorithm has good results [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…It is necessary to study the previous results for this project. Li summarizes the development and status of target detection algorithms [ 5 ], which gives us a deeper understanding of target detection algorithms. Ting et al merged the feature extraction process with the Ghostbottlenet algorithm to improve the accuracy of the YOLOv5 algorithm to solve the problem of insufficient feature extraction in current ship identification methods.…”

Section: Introductionmentioning

confidence: 99%

Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5

Yao

Fan

et al. 2022

Sensors

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With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability of self-driving vehicles in harsh environments, we first construct a new color levels offset compensation model to perform adaptive color levels correction on images, which can effectively improve the clarity of targets in adverse weather and facilitate the detection and recognition of targets. Then, we compare several common one-stage target detection algorithms and improve on the best-performing YOLOv5 algorithm. We optimize the parameters of the Backbone of the YOLOv5 algorithm by increasing the number of model parameters and incorporating the Transformer and CBAM into the YOLOv5 algorithm. At the same time, we use the loss function of EIOU to replace the loss function of the original CIOU. Finally, through the ablation experiment comparison, the improved algorithm improves the detection rate of the targets, with the mAP reaching 94.7% and the FPS being 199.86.

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Summary of Target Detection Algorithms

Cited by 28 publications

References 6 publications

Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model

Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model

Human Position Detection Based on Depth Camera Image Information in Mechanical Safety

Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5

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